Bugs in Theano and Tensorflow?

Question

Python

Studying machine learning (self-taught) I accidentally encountered such a problem.
Why does the same NS on different backends work, then it gives an error.
For example.
I took the usual NS from the Internet (some lessons).
And found that
Theano backend:
Everything works well.
And Tensorflow backend:
Throws an error.
TypeError: 'int' object is not iterable.

from numpy import array
from numpy import hstack
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import LSTM
from keras.preprocessing.sequence import TimeseriesGenerator
# define dataset
in_seq1 = array([10, 20, 30, 40, 50, 60, 70, 80, 90, 100])
in_seq2 = array([15, 25, 35, 45, 55, 65, 75, 85, 95, 105])
# reshape series
in_seq1 = in_seq1.reshape((len(in_seq1), 1))
in_seq2 = in_seq2.reshape((len(in_seq2), 1))
# horizontally stack columns
dataset = hstack((in_seq1, in_seq2))
# define generator
n_features = dataset.shape[1]
n_input = 2
generator = TimeseriesGenerator(dataset, dataset, length=n_input, batch_size=8)
# define model
model = Sequential()
model.add(LSTM(100, activation='relu', input_shape=(n_input, n_features)))
model.add(Dense(2))
model.compile(optimizer='adam', loss='mse')
# fit model
model.fit_generator(generator, steps_per_epoch=1, epochs=500, verbose=0)
# make a one step prediction out of sample
x_input = array().reshape((1, n_input, n_features))
yhat = model.predict(x_input, verbose=0)
print(yhat)

Why is not clear.
I searched the Internet for an answer.
It seems that the error is related to the for(range) loop. but there is no such thing.
What to do?